Recognizing human grasping strategies is an important factor in robot teaching as these strategies contain the implicit knowledge necessary to perform a series of manipulations smoothly. This study analyzed the effects of object affordance-a prior distribution of grasp types for each object-on convolutional neural network (CNN)-based grasp-type recognition. To this end, we created datasets of first-person grasping-hand images labeled with grasp types and object names, and tested a recognition pipeline leveraging object affordance. We evaluated scenarios with real and illusory objects to be grasped, to consider a teaching condition in mixed reality where the lack of visual object information can make the CNN recognition challenging. The results show that object affordance guided the CNN in both scenarios, increasing the accuracy by 1) excluding unlikely grasp types from the candidates and 2) enhancing likely grasp types. In addition, the "enhancing effect" was more pronounced with high degrees of grasp-type heterogeneity. These results indicate the effectiveness of object affordance for guiding grasp-type recognition in robot teaching applications.

中文翻译：

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对象提供能力作为抓握类型识别的指南

认识到人类的抓握策略是机器人教学中的重要因素，因为这些策略包含了平滑执行一系列操作所必需的隐性知识。这项研究分析了对象供给能力的影响-基于卷积神经网络（CNN）的抓握类型识别的每个对象的抓握类型的先验分布。为此，我们创建了标有抓握类型和对象名称的第一人称抓握图像数据集，并测试了利用对象提供能力的识别管道。我们评估了要把握的真实和虚幻物体的场景，以考虑混合现实中的教学条件，其中缺乏视觉物体信息会使CNN识别具有挑战性。结果表明，在两种情况下，对象承受能力均指导了CNN，通过1）从候选者中排除不太可能的抓握类型和2）增强可能的抓握类型来提高准确性。此外，“抓握效果”随着抓握型异质性的提高而更加明显。这些结果表明，在机器人教学应用中，对象供给对于指导抓握类型识别的有效性。